A Novel Approach for Vehicle Detection Using an AND–OR-Graph-Based Multiscale Model

In this paper, a novel approach for detecting multiscale vehicles with time-varying vehicle features based on a multiscale and-or graph (AOG) model is proposed. Our approach consists of two steps, i.e., construction of a multiscale AOG model and an inference process for vehicle detection. The multiscale model uses global features to describe low-scale vehicles and local features to represent high-scale vehicles. Meanwhile, multiple appearances, such as sketch, flatness, texture, and color, are used to represent the global and local features. By virtue of the use of both global and local features as well as multiple appearances, our model is more suitable for describing multiscale vehicles in complex urban traffic conditions. Based on this multiscale model, an inference process using local features (local process) is integrated with a process using global features (global process) to detect multiscale vehicles. To evaluate the performance of our proposed method, a validation experiment, a quantitative evaluation, and a contrasting experiment are conducted. The experimental results show that our proposed approach can efficiently detect multiscale vehicles. In addition, the results also demonstrate that our approach is able to handle partial vehicle occlusion and various vehicle shapes and has great potential for real-world applications.